• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.5
• /
• pp.2261-2266
• /
• 2011

Being the most widely used in ultrasound Doppler color flow mapping, the Kasai algorithm, also known as lag-1 autocorrelation method, is capable of estimating the Doppler mean frequency relatively accurately with a modest amount of computation. Particularly in the case of imaging deep lying areas, however, its performance suffers due to low signal-to-noise ratios. The purpose of this paper is to propose a dealiased lag-2 autocorrelation method which is superior to the Kasai algorithm even at low signal-to-noise ratios and to compare their performances through simulations. The proposed algorithm is found to be better by about 2 to 3 dB than the Kasai algorithm in terms of Doppler mean frequency estimation error in the presence of measurement noise.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
• /
• 2004.04a
• /
• pp.455-458
• /
• 2004

This study uses time series analyses to evaluate fluctuation of water levels in a geothermal water well due to pumping, in relation to rainfall at Dongrae hot-spring site on the southeastern coast of tile Korean peninsula. The volume of water pumped from the public study wells ranges from 542 to 993 m$^3$/month, and the minimum water level ranged from 35 to 144.7 m during the measured period. Autocorrelation analysis was conducted for the withdrawal rate at the public wells, water levels and rainfall. The autocorrelation of the withdrawal rate shows distinct periodicity with 3 months of lag time, the autocorrelation of rainfall shows weak linearity and short memory with 1 months of lag time, and the autocorrelation of water levels shows weak linearity and short memory with 2 months of lag time. The cross-correlation between the pumping volume and the minimum water level shows a maximum value 1 at a delayed time of 34 months. The cross-correlation between rainfall and the minimum water level shows a maximum value of 0.39 at a delayed time of 32 months.

• Journal of Korean Society of Forest Science
• /
• v.101 no.2
• /
• pp.203-212
• /
• 2012

This study aims to test presence of spatial autocorrelation of burn severity in Uljin and Youngduk areas burned in 2011. SPOT satellite images were used to compute the NDVI representing burn severity, and NDVI values were sampled for 5,000 randomly dispersed points for each site. Spatial autocorrelations of sampled NDVI values were analyzed with Moran's I and Variogram models. Moran's I values of burn severity in Uljin and Youngduk areas were 0.7745 and 0.7968, respectively, indicating presence of strong spatial autocorrelations. On the basis of Variogram and changes of Moran's I values by lag class, ideal sampling distance were proposed, which were 566-2,151 m for Uljin and 272-402 m for Youngduk. It was recommended to apply these ranges of sampling distance in flexible corresponding to Anisotropic characteristics of burned areas.

• Journal of Environmental Health Sciences
• /
• v.14 no.1
• /
• pp.39-45
• /
• 1988

The stochastic variations were analyzed periodicity by autocorrelation, variance spectrum and Fourier series. These time series included hourly and hourly mean observations on DO, water temperature and air temperature which measured by automatic recording instrument at Guii from 1, Jan., 1986 to 23, Feb., 1986. The results of study were as follows: l. Autocorrelation coef. (lag time 120) DO($\varrho_1$= 0.9705), WT($\varrho_1$ = 0.9890), and AT($\varrho_1$ = 0.9874) were deeply related. DO and AT clearly showedr 24-hour periodicities while WT showed 23-26 hour periodicity. 2. Spectral density showed high at 24 hour in eech item and all of them showed weak peak at 12 hour. 3. The explained variance, which was a measure of the contribution of periodic function to the original time series, varied high 90.8 - 94.7%. This results showed that water qualities at Guii were affected deterministic components.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
• /
• 2004.04a
• /
• pp.447-450
• /
• 2004

This study was conducted to characterize groundwater and river-water fluctuations at a riverbank filtration site in Daesan-myeon adjacent to the Nakdong River, using time series analysis. Water levels from six observation wells from January 2003 to October 2003 were measured. The autocorrelation analysis indicates that the wells are divided into three groups: group 1 represents strong linearity and memory, group 2 intermediate linearity and memory, and group 3 weak linearity and memory. The analysis indicates that groundwater levels in different monitoring wells vary in response to river-water levels, groundwater withdrawal and seasonal rainfall. Cross-correlation was also divided into three groups. Group 1 shows the highest cross-correlation function (0.49 - 0.54) for a lag time of 0 hours, group 2 intermediate cross-correlation function (0.34 - 0.45), and group 3 the lowest cross-correlation function (0.23 - 0.25). Different cross-correlation functions among the 3 groups are interpreted as an effect of tile distance from the river to the pumping wells.

• Journal of Preventive Veterinary Medicine
• /
• v.41 no.2
• /
• pp.79-83
• /
• 2017

In this study, we used a choropleth map to explore the spatial variation of the risk of cattle herds being bovine tuberculosis (BTB) positive in Gangwon-do in 2015. The map shows that the risk of being BTB-positive was lower in provinces located in the middle of Gangwon-do (Wonju, Youngwol, Peongchang, and Kangneung) than in other provinces. In addition, one province located in the north (Goseong) had a low risk of BTB. The estimate for the intercept of the spatial lag model was 0.66, and the spatial autocorrelation coefficient (lambda) was 0.20 (Table 1). The Moran's I was 0.33 with p-value of 0.02. In 2015, provinces located in the North West (Hwacheon) and East (Donghae) of Gangwon-do had a higher BTB risk. We identified some specific provinces at low BTB-positive risk, information that may prove useful for control of BTB in the study area.

• Environmental Engineering Research
• /
• v.22 no.3
• /
• pp.302-311
• /
• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• Water Engineering Research
• /
• v.2 no.2
• /
• pp.139-148
• /
• 2001

The teleconnections between El Nino/Southern Oscillation (ENSO) and droughts in Korea and the continental United States(U.S.) are investigated using cross analysis. For this purpose, monthly ENSO data and Palmer Drought Severity Index (PDSI) for Korea and for seven states in the U.S. are used. This study shows that there are significant statistical associations between ENSO indices and PDSI for Korea; however, the associations are very weak. It is found that dry conditions in Korea are positively correlated with El Nino, while wet conditions with La Nina. SOI, SSt in the Nino 4 and Ship track 6 regions among ENSO indices are more strongly correlated with PDSI than the other ENSO indices when using the original standardized data, but the SST Nino 3, SST Nino 4, and Darwin SSP exhibit abetter correlations with PDSI when using filtered data to be removed autocorrelation components of the original standardized data. The response time lag for maximum correlation between ENSO indices and PDSI appears to be affected by filtering the data. This is expecially true for Korea than for state analyzed in U.S. In addition, it is found that the PDSI in the continental U.S. is more strongly correlated wiht ENSO than in Korea. Furthermore, in analyzing the El Nino and La Nina aggregate composite data, it is found that the dry anomalies in Korea occur from the year following El Nino to about tow years after while the wet anomalies occur from La Nina year for a period of about two years.

• Journal of KIBIM
• /
• v.11 no.2
• /
• pp.43-53
• /
• 2021

The 'public agency oil joint purchase system' was introduced to lower public sector oil prices and contribute to the stability of the overall consumer oil market. The present study used spatial regression to analyze the factors affecting domestic gasoline price, focusing on the impact of potential implicit collusion among gas stations in determining domestic gasoline prices. Also, this study investigated the effect the location characteristics of the market convention gas stations and government offices on the pressure of price competition in the market and the gasoline price at general gas stations. To summarize the results of the spatial lag model (SLM), the individual characteristics of gas stations such as convenience stores (+), self-fuelling (-), commercial areas (+), subway stations (+), population density (-), and sales (-) are correlated to gasoline prices at gas stations, and the institutional location factors of gas stations (+) affected the average of 9 won per liter, 11 won per liter. In order to solve these problems, the establishment of a monitoring system reflecting the location characteristics of the region and the ongoing review of the system should be carried out. In addition, separate, expanded and promotional measures should be prepared for the convenience of general and public oil buyers.

• Journal of Korea Water Resources Association
• /
• v.39 no.12 s.173
• /
• pp.997-1012
• /
• 2006

We have presented a nonparametric stochastic approach for the SOI(Southern Oscillation Index) series that used nonlinear methodology called Nonlinear AutoRegressive(NAR) based on conditional kernel density function and CAFPE(Corrected Asymptotic Final Prediction Error) lag selection. The fitted linear AR model represents heteroscedasticity, and besides, a BDS(Brock - Dechert - Sheinkman) statistics is rejected. Hence, we applied NAR model to the SOI series. We can identify the lags 1, 2 and 4 are appropriate one, and estimated conditional mean function. There is no autocorrelation of residuals in the Portmanteau Test. However, the null hypothesis of normality and no heteroscedasticity is rejected in the Jarque-Bera Test and ARCH-LM Test, respectively. Moreover, the lag selection for conditional standard deviation function with CAFPE provides lags 3, 8 and 9. As the results of conditional standard deviation analysis, all I.I.D assumptions of the residuals are accepted. Particularly, the BDS statistics is accepted at the 95% and 99% significance level. Finally, we split the SOI set into a sample for estimating themodel and a sample for out-of-sample prediction, that is, we conduct the one-step ahead forecasts for the last 97 values (15%). The NAR model shows a MSEP of 0.5464 that is 7% lower than those of the linear model. Hence, the relevance of the NAR model may be proved in these results, and the nonparametric NAR model is encouraging rather than a linear one to reflect the nonlinearity of SOI series.